System for capturing graphical images using hyperspectral illumination

ABSTRACT

A graphical scanner for scanning a graphical image includes a source for producing an optical beam, a monochromator for dividing the optical beam into a plurality of component beams for hyperspectral bandpasses, a director for directing the component beams to illuminate portions of the graphical image, a sensor for measuring a light intensity for the one or illuminated portions, and a translator for transforming the measured light intensities for each of the one or more portions into hyperspectral traces each representing a spectral power distribution. The translator further transforms the hyperspectral traces into one or more device-independent representations of color.

FIELD OF THE INVENTION

The present invention relates to the field of technical reproduction ofgraphical images. In particular, it relates to a system for measuringand transforming light from individual elements, or pixels, thatcomprise an image, to produce a hyperspectral trace, representing aspectral power distribution, that can be further transformed by thesystem into a number of device-independent color space formats,including device-independent color space formats defined by theCommission Internationale de l'Eclairage (CIE).

BACKGROUND OF THE INVENTION

Colorimetry, or the science of color, has evolved over several centuriesof experimentation to develop robust models for specifying human colorperception. A good summary of the foundations of color science isprovided in U.S. Patent Publication US-2002-0159098-A1, entitled“Hyperspectral System For Capturing Graphical Images”, published on Oct.31, 2002 and hereby incorporated by reference.

Conventional apparatus for capturing colored graphical images utilize amethod based upon an industrial implementation of a central colorscience concept, the Trichromatic Generalization, which explains howcolors mix and match. In the conventional scheme, a coordinate systemcharacterized as a Device Dependent Color Space (DDC) utilizes linearmixtures of three arbitrary primary colors to match the color ofindividual pixels of the original.

The origin of the scientific Trichromatic Generalization has its basisin human physiology. The sensation of color is a complex interaction ofthe human nervous system with light, electromagnetic radiation foundbetween the wavelengths of 300 nm and 830 nm. Ordering the psychologicaldesignations of color perception creates the visible spectrum, fromshort to long wavelengths, violet, blue, green, yellow, orange, and red.The color matching rules of the Trichromatic Generalization are used topredict how mixtures of the different wavelengths are perceived byhumans. Complicating the mechanical aspects of color perception arevisual system anomalies.

The human eye's lens brings different wavelengths of light to focus atdifferent distances behind the lens and absorbs almost twice as muchblue light as yellow or red, resulting in a relative insensitivity toshorter wavelengths, a condition exaggerated by age. The light thatfinally passes through the eye strikes the retina, a small area at theback of the eye densely packed with individual light sensitive receptorsconnected to the optic nerve, the conduit that transmits and processesvisual sensations from the eye to the visual cortex in the brain. It hasbeen shown the light sensitive photoreceptors are of two kinds, rods,which function at night or at very low light levels, and cones, whichfunction under daylight conditions and are the sole source of colorperception sensations in humans. The cones are circularly situated atthe center of the eye's focal area, the fovea, with the rods forming aring around the cones.

The notion of “tri” associated with the Trichromatic Generalizationarises from the relative sensitivity of the three different cone typesgenerally accepted to be found within the fovea. About 64% of conesexhibit peak sensitivity to 575 nm wavelength light and are said to bered sensitive, though the 575 nm bandpass is actually perceived asyellow. Thirty two percent of cones are considered green, most sensitiveto 535 nm light, and only two percent are blue, having a peak responseat about 445 nm. It is generally believed analyzing the ratio of theneural activities generated by visually stimulating the three differentphotoreceptors is the method by which the human visual system interpretscolor. In practice, it has been shown that the channels of informationfrom the three cones are transformed into three new so-called opponentchannels, transmitting a red to green ratio, a yellow to blue ratio anda brightness factor, based upon red and green only, to the brain'svisual cortex. The physiological sensations produced by visual stimulusare thought to be correlated with stored psychological perceptions,creating color vision.

The above described physiology allows perception of the physical aspectsof color, electromagnetic radiation found between the wavelengths of 380nm and 780 nm, referred to here as human-visible light. Physically,color perception varies according to the wavelength of the visualstimulus. Wavelength is calibrated in nm (nanometer) denominated units,with groups or multiple wavelengths described as bandwidth. When thebandpass of the bandwidth is narrow, the resulting perceptions areassociated with pure, or highly saturated, color. As the observedbandpass widens, the color appears less pure. Observers with normalcolor vision generally identify pure blue as light with a wavelength ofabout 470 nm, pure green as light with a wavelength of about 535 nm,pure yellow as 575 nm light, and pure red as 610 nm light. However,individual observers often respond differently to the same specimen, sowhat is a pure color to one may not be perceived that way by anotherobserver.

Besides wavelength, other important physical attributes of visible lightare luminance, illuminance, transmittance (reflectance) and metamerism.Luminance accounts for light emitted, such as from a computer display,calibrated in units that reflect the eye's uneven sensitivity todifferent wavelengths. Illuminance is a measurement of the amount oflight that falls on an observed object and transmittance (reflectance)is the measurement of light photons that are absorbed and regenerated asnew photons in proportion to the amount of original photons thattransmitted through (reflected off) the surface of the object. Variouswavelengths of light that are absorbed and retransmitted through(reflected off) a measured image (or specimen) and presented as apercentage of the wavelengths of light that initially struck it can bedescribed as the image's (specimen's) characteristic spectraltransmittance (reflectance) curve.

It is useful to consider that the reproduction of a colored image may bethought of as an exercise in color matching which takes into account thespectral power distribution of the light source (ie: viewing conditions)illuminating the original, the characteristic curve of the original, thepower distribution of the light source illuminating the reproduction,and the characteristic curve of the reproduction. When thecharacteristic curve of the source's power distribution is combined withthe spectral transmittance of the specimen, a visual stimulus is createdwhich triggers color perception. Mathematically characterizing the colorperception triggered by the combination of a source's power distributionand a specimen's transmittance curve is a necessary first step insuccessfully reproducing the perception.

There is, however, a phenomenon that impacts color perception andtherefore color reproduction; metamerism. To illustrate the phenomenon,consider two specimens with identical characteristic curves. They willappear to the average observer to match under any source of illuminance.Now, consider two specimens with different curves. They will appear tovary with regards to one another as the source of the illumination isvaried. However, there can be two specimens that appear to match despitehaving different characteristic curves. This is metamerism. An exampleof metamerism is when the two specimens with different characteristiccurves are observed under different sources of illumination, and a matchis observed under one of the sources. Because the reproduction ofcolored images entails taking into account different viewing conditionsand media, the mathematical characterization of a color perceptiondestined for reproduction must take into account metameric matches. Acolor measurement system capable of identifying and predictingmetamerism is the CIE system (devised by the Commission Internationalede l'Éclairage).

The CIE system includes non-linear color model transformations andprocedures to account for different viewing conditions and visualphenomena such as metamerism and color contrast. And, to simplify colormatching, the CIE system uses mathematical means, imaginary primariesdesignated X, Y and Z, to eliminate color matching possibilities thatrequire a minus primary value to make a match. The X, Y and Z primariescreate a superset of color which includes all colors a human mightperceive. This is a key difference as compared to the physical primariesintegrated into current graphical imaging systems, whose color gamut (orrange of producible colors) is a subset of human color perception.

The three primary colors X, Y and Z utilized by the device independentCIE color model are mathematical abstractions based upon statisticalanalysis of the response of different observers to color specimenscompared in a highly standardized manner. For example, the CIE hasdefined a standard manner for observing a color match which requiresobserving a structure free specimen field that subtends 2° of arc whenpositioned 45 cm (18 inches) from the eye's iris. By correlating theresults of these observations with precise and accurate measurements ofa visual stimuli's physical color properties, a device independentsystem able to correctly measure human color perception is created.

Devices currently utilized to quantify color for reproduction means usecolor systems that require actual samples of real primary colors(usually red, green and blue, i.e. R, G, B) be present to makemeasurements. Light is transmitted through a colored object and throughfilters that isolate the primary colors. Upon exiting the primaryfilters the light, effected by the optical density and color of theobject, as well as the three primary color filters, is measured andnoted as three integer values, one each for the R, G and B primarycomponent created by the device for the object measured. This methodcreates a measurement process tied to a specific physical color space,with all the inherent color gamut limitations of physical rather thanimaginary primaries. The methods and techniques used to create andmeasure the R, G and B components of a physical color space vary fromvendor to vendor and are without any common standards.

Although a convenient way to describe colors, the limitation of anydevice dependent system is that regardless of how the three primarycolors are chosen, observer metamerism effects (where two objects appearto some observers or devices to have the same color, but to otherobservers or devices the same objects do not match) cannot beeliminated. Values expressed by a device dependent color system areaccurate only within a truncated color space, and only if the exact samefilters, lights, inks or pigments used to render a particular color areused as the physical primaries in the measuring device, which is animpossibility. That being the case, it has been recognized that moreinformation than is contained in a device dependent color model isneeded to produce accurate color reproduction.

Despite it's known inaccuracy, device dependent color-based measuringand rendering systems have been integrated into virtually all industrialand commercial applications related to the processes that are calledupon to reproduce full color images, such as printing, photography andtelevision. Over generations the conflict of accurately measuring andrendering with physical color systems has lead to extensive tradepractices being established. These practices, commonly referred to as“color correction,” integrate human judgment with the physical colorsystems in a way that requires humans to make decisions to resolve ormask the inherent limitations of a physical color system. In physicalcolor image scanning methods, humans are expected to compensate fordifferences between the color content of the original image, what ascanner can capture of the original color content, how the scannerdescribes what it captured, and how the captured data must be adjustedfor use by various digital, xerographic and lithographic renderingprocesses.

By agreement, the CIE, (Commission Internationale de l'Eclairage), since1913, has developed standards regarding how the TrichromaticGeneralization is interpreted, as well as how color is measured anddescribed. The underlying premise of the CIE system, referred to asCIE-31, is that the stimulus for color is provided by the propercombination of a source of light, an object, and an observer. In 1931the CIE introduced standardization of the source and observer and themethodology to derive numbers that provide a measure of a color seenunder a standard source of illumination by a standard observer. Thisstandardization forms the foundation of modern colorimetry. CIE-31 usesa specimen's Characteristic Curve for the calculation of TristimulusValues X, Y, and Z and Chromaticity Coordinates x and y. The CIE-76recommendations establish transformations of the X, Y, and Z TristimulusValues into nearly visually uniform color scales such as CIELAB, andalso established a method to quantify differences between two colorspecimens.

CIELAB (L*a*b*), the result of a non-linear transformation of X, Y andZ, is an opponent-type system that assumes a color cannot be red andgreen at the same time, or yellow and blue at the same time, though itcan be both red and yellow (ie: orange) or red and blue (ie: purple).Therefore, a specimen's redness or greenness can be expressed as asingle number, called a*, which is positive if the color is red andnegative if it is green. It follows that yellowness or blueness isdesignated by the coordinate b*, positive for yellow and negative forblue. The third coordinate, L*, is the lightness of the color.

The full benefit of the CIE system has not been taken advantage of bythe graphic arts industry with regards to image scanning. Recently,devices capable of measuring 1 nm and 5 nm wide bandpasses of radiantenergy (sometimes referred to as “hyperspectral” in the literature) havebeen developed (see, e.g. U.S. Patent Publication US-2002-0159098-A1).For example, the graphical image scanner disclosed in U.S. PatentPublication US-2002-0159098-A1 includes a light source to illuminate thegraphical image, a collector to segment the image into a plurality ofpixels and collect light emanating from the plurality of pixels, ahyperspectral analyzer to divide the collected light into a plurality ofhyperspectral bandpasses and measure a light intensity for each of thehyperspectral bandpasses, a calculator to transform the measured lightintensities for the plurality of hyperspectral bandpasses into adevice-independent representation of color for each of the pixels, aprocessor with stored program control to format the device-independentcolor representations for the plurality of pixels as a digital datafile, and a memory for storing the digital data file. This scanner doeshowever incorporate complex electronic and electro-optical hardwarewhich result in a scanner cost and footprint that exceeds requirementsfor may smaller enterprises. Accordingly, it would be desirable todevelop a hyperspectral graphical image scanner of reduced size and costsuitable for smaller enterprises.

SUMMARY OF THE INVENTION

Disclosed is a method and apparatus for creating a digital master of agraphical image in a hyperspectral form. The digital master created inhyperspectral form may be further transformed into 1)device-independent, scientific calorimetric notation defined by theCommission Internationale de l'Eclairage (CIE) and 2) device-dependentcalorimetric notation, or left untransformed in the form of 3)calorimetric characteristic curves. Each of the transformed anduntransformed forms may be stored in a specialized data file or bufferfor further use.

The disclosed apparatus, in one embodiment, is a graphical image scanneroptimized to capture film-based graphical images. The purpose of agraphical image scanner is to determine and save, at a pre-selectedspatial resolution, a color value for each individual picture element,or pixel, of an image.

A novel aspect of the disclosed method and apparatus is the use of ahyperspectral source of illumination, which allows measurement of therelative hyperspectral power distribution of a pixel's light, thereforequalifying the use of CIE-defined device-independent datatransformations to determine pixel color.

In one disclosed embodiment of the invention, a method is defined tocomprise the steps of: a) dividing a light beam from an illuminationsource into a plurality of component beams each representing one of aplurality of hyperspectral bandpasses, wherein the plurality ofhyperspectral bandpasses define a spectrum characterized by wavelengthsranging continuously between 360 and 830 nanometers, and wherein thecomponent beam for each hyperspectral bandpass is characterized by asubstantially unique and non-overlapping selection of continuouswavelengths from the spectrum, b) successively illuminating one or moreportions of the graphical image with each of the plurality of componentbeams, c) measuring a light intensity with a sensor for each of the oneor more illuminated portions of the graphical image with respect to eachof the plurality of component beams, d) transforming each measured lightintensity into a relative light intensity based on minimum and maximumlight intensities measured by the sensor, and e) saving the relativelight intensities in a buffer as a hyperspectral trace. The savedhyperspectral trace may then be further transformed to produce one ormore CIE device-independent color models for the image. Thehyperspectral trace may also be transformed to produce one or more RGBdevice-dependent color models for the image.

BRIEF DESCRIPTION OF THE DRAWINGS

A more complete understanding of the invention may be obtained byreading the following description of specific illustrative embodimentsof the invention in conjunction with the appended drawing in which:

FIG. 1 provides a schematic diagram illustrating the principlecomponents of an embodiment of the present invention, and theirinterrelationships;

FIG. 2 illustrates a first image magnification as provided by lens 20 ofFIG. 1;

FIG. 3 illustrates a second image magnification as provided by lens 19of FIG. 1;

FIG. 4 provides a schematic diagram illustrating sensor 24 of FIG. 1;

FIG. 5 provides a schematic diagram further illustrating components ofthe embodiment of FIG. 1, including an associated host computer system,a digital signal processor (DSP), an analog to digital converter (ADC),servo control circuitry and data processing circuitry;

FIG. 6 further illustrates the data processing circuitry of FIG. 5, aswell as fixed and variable buffers, data files and other logic elementsassociated with the data processing circuitry of FIG. 5;

FIG. 7 further describes the fixed and variable buffers of FIG. 6; and

FIGS. 8-11 illustrate successive steps in a pipelining process employedby the data processing circuitry of FIGS. 5 and 6.

In the various figures, like reference numerals wherever possibledesignate like or similar elements of the invention.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

The following detailed description includes a description of the bestmode or modes of the invention presently contemplated. Such descriptionis not intended to be understood in a limiting sense, but to be anexample of the invention presented solely for illustration thereof, andby reference to which in connection with the following description andthe accompanying drawing one skilled in the art may be advised of theadvantages and construction of the invention.

The disclosed spectral scanner creates pixels optically. With referenceto FIG. 1, the output 6 of a white light source 2 is directed by areflector 1 along an optical path through an iris diaphragm 4 controlledby a servo motor 3. Iris diaphragm 3 regulates the overall intensity ofthe white light entering the hyperspectral bandpass creation apparatus 5(for example, a monochromator). Mirrors 25 in the apparatus 5 direct theoutput of the white light source 2 (for example, a continuous portion ofthe electromagnetic spectrum located between 360 nm and 830 nm) toreflect off an optical grating 8. The grating is capable of separatingthe white light 6 into a plurality of narrow hyperspectral bandpasses oflight 7 containing continuous segments of the light spectrum, forexample, 1 nm or 5 nm in width. These hyperspectral bandpasses 7 exitthe apparatus 5 and are condensed and focused by optics 11 and formedinto a spot by an iris diaphragm 10 controlled by a servo motor 9. Thespot is deflected off of a scanning mirror 12 controlled by a servomotor 13, which sweeps the spot, for example, across a 1 mm high slit16.

The slit 16, aligned in the optical path in a fixed position, is builtinto a translation stage 15 controlled by a servo motor 14. The stage 15holds an image 17 (for example, a standard 35 millimeter (mm) slide filmimage) perpendicular to the optical path and behind the slit as it stepsthe image through the optical path under the control of the applicationsoftware 29 further illustrated in FIGS. 5 and 6. Therefore, as the spotis swept across the translation stage slit 16, it illuminates a specificportion of the image (designated the “scan line”). Depending on themagnification factor option selected by the user, the scan line may befocused by one or more of lenses 19, 20 mounted as movable stages ontracks 21. This arrangement allows the lenses 19, to be moved into andout of the optical path, for example, by a servo motor 18. The scan lineis projected by lenses 19, 20 onto an area light sensor 24 mounted on atranslation stage 23 controlled by a servo motor 22.

FIG. 2 illustrates the relationship of the optical components when themagnification factor is 100%, and FIG. 3 illustrates the relationship ofthe optical components when the magnification factor is 163%. Theorientation of the image, magnification factor and spatial resolution ofthe individual light sensitive elements of the area sensor determine thespatial scanning resolution achieved by the device. Lenses 19, 20 arecontrolled, for example, by magnification lens change servo 18 of FIG. 5via host computer system 30, application software 29, servo amplifierbus 31 and servo amplifier 33.

To illustrate, a 35 mm image (image 17 represented by ray AB in FIG. 2)with a horizontal dimension of 1.35 inches and a vertical dimension of0.92 inches is inserted into the translation stage 15 with its longeraxis parallel to the slit (“landscape” mode). With lens 20 set asillustrated in FIG. 2 (100% magnification), a 1 mm high by 1.35 inchwide image of the scan line is projected onto the area sensor 24(represented as ray A′B′ imaged on sensor 24 in FIG. 2). In FIGS. 2 and3, according to convention, F, F′, F″ and F″′ represent focal lengths ofthe associated lenses 20, 19, and S, S′, S″, S″′ each represent anassociated 100% magnification point (twice the associated focal length).

The area sensor 24 of FIGS. 1, 4 and 5 may be, for example, composed offour quadrants, AS, BS, CS and DS. Quadrants AS and BS are configured todirectly receive light transmitted via lenses 19, 20, and quadrants CSand DS act as transfer buffers for transferring light signals to themultiplexer 26 of FIGS. 5 and 6. This arrangement improves the sensor'sthroughput by allowing the device to both capture image data andtransfer image data simultaneously and in parallel. Sensor 24 may beproperly aligned with the transmitted light image by causing hostcomputer system 30 and application software 29 to control sensortranslation stage servo 22 via servo amplifier 32.

Each quadrant of the sensor 24 may contain, for example, 1,242,060 5 μm(μm=micron) dimensioned sensor elements arranged in 3810 columns and 326rows, creating an active image area of 7620×326 pixels, 1.5 inches wideby 1.63 mm high. Each transfer area of the sensor 24 is of equal size.Therefore, when the landscape mode image is projected onto the sensor at100% magnification, the scan line is reduced to a 6,858×200 pixel matrixand the image is scanned at an optical resolution of 5,080 pixels perinch.

When the image 17 is inserted into the translation stage 15 in portraitmode, the shorter axis of image 17 is parallel to the slit 16, and theoptics are adjusted as illustrated in FIG. 3. (image 17 in FIG. 3illustrated as image line AABB), the image is magnified by 163%.Therefore, the 0.92 inch by 1 mm scan line is projected onto the fullactive area of the sensor, 1.5 inches×1.63 mm (the image on sensor 24illustrated in FIG. 3 as image line AA′BB′) a matrix of 7,620×326pixels, and the image is scanned at an optical resolution of 8,283pixels per inch.

The scanning process, or movement and measurement of the image, occursin a multi-part automated cycle controlled by the application software29 and host computer system 30, as illustrated for example in FIGS. 5and 6. First the image settles, then the system illuminates the image bysystematically sweeping a plurality of spots, composed of hyperspectralbandpasses between the wavelengths of 360 nm and 830 nm, across the scanline. Host computer system 30 causes the sweeping of spots bycontrolling intensity iris diaphragm servo 3 via servo amplifier 35,spot iris diaphragm 9 via servo amplifier 36, and spot scanner servo 13via servo amplifier 37, as well as by manipulating monochromator 5.

As the spot illuminates the scan line, the sensor 24 both determines theintensity of the light transmitted through the image and breaks the scanline into pixels corresponding to individual scan elements of the sensor24. Signals are output from the individual scan elements of sensor 24,multiplexed by multiplexer 26, and converted to digital form by analogto digital converter (ADC) 27. The resulting digital signals aretemporarily stored and processed in bandpass (λ) delineated buffers 50,51, 52, 53 and 54 of variable buffers 38 as shown in FIGS. 6 and 8, scanline by scan line, in alternating pipelined hardware buffers (“B Pipes”)as illustrated in FIGS. 8-11. By means of application software 29 anddigital signal processor (DSP) 28, the system may for examplesimultaneously perform normalization and CIE-defined mathematicaloperations on the stored data in one pipe as it continues to capture andfill the alternative pipe with more bandpass delineated pixel intensitydata.

After the scan line has been illuminated with all appropriatehyperspectral bandpasses of light, the host computer system 30 of FIG. 5moves the image to bring a new 1 mm high scan line into the optical pathby controlling image translation stage servo 15 via servo amplifier 34.As it brings the next scan line into the optical path, the DSP 28 andapplication software 29 store the processed pixel data in buffers 38.

The user can direct the system to save data as a colorimetriccharacteristic curve representing the spectral power distribution ofeach pixel and/or have the system transform and save thepixel-delineated spectral power distribution data as CIE-defined ornon-CIE colorimetric Tristimulus values.

Typically, it takes the system 125 mS (milliseconds) to both sweep aspot across the slit and then switch bandpasses when in portrait mode,and 185 mS when in landscape mode. When using bandpasses calibrated to a5 nm spectral resolution, the system makes 95 sweeps per scan line, fora total scan time per scan line of 11.88 seconds in portrait mode and17.58 seconds in landscape mode. In portrait mode at 163% magnification,the system analyzes 2,484,120 pixels per scan line, or 209,101 pixelsper second. In landscape mode at 100% magnification, the system analyzes1,371,600 pixels per scan line, or 78,020 pixels per second. As thereare 35 scan lines in a 35 mm image in portrait mode and 24 in landscapemode, it takes 6.93 minutes to scan the image in portrait mode and 7.03minutes in landscape mode.

The disclosed system uses CIE-defined specifications to measure andtransform objects such as pixel light into color values. The CIE systemassumes that the Stimulus for Color is provided by the propercombination of a Source of Light, an Object, and an Observer. Some timeago the CIE, at set wavelength intervals (λ) calibrated in nm,mathematically standardized Sources of Light via Power DistributionTables for Standard Illuminants (S) and standardized Observers via ColorMatching Function Tables for Standard Observers (x, y, and z). The CIEalso developed a methodology that uses Standardized Illuminants,Standardized Observers and the Relative Spectral Power Distribution (T)of the Object to derive numbers that are designated the ColorimetricTristimulus Values X, Y and Z, and which provide a standard measure ofan Object's color. This methodology is mathematically expressed as:

$\begin{matrix}{X = {k{\sum\limits_{360}^{830}{T_{(\lambda)}S_{(\lambda)}{\overset{\_}{x}}_{(\lambda)}}}}} & \lbrack 1\rbrack \\{Y = {k{\sum\limits_{360}^{830}{T_{(\lambda)}S_{(\lambda)}{\overset{\_}{y}}_{(\lambda)}}}}} & \lbrack 2\rbrack \\{Z = {k{\sum\limits_{360}^{830}{T_{(\lambda)}S_{(\lambda)}{\overset{\_}{z}}_{(\lambda)}}}}} & \lbrack 3\rbrack\end{matrix}$where k is a normalization constant:

$\begin{matrix}{k = {100/{\sum\limits_{360}^{830}{S_{(\lambda)}{\overset{\_}{y}}_{(\lambda)}}}}} & \lbrack 4\rbrack\end{matrix}$

and where specific tables for spectral power distribution S_(λ) andcolor matching functions x; y and z are all functions of the CIE-definedwavelength interval (λ). The bandpasses created for T_(λ) by sweeping aplurality of hyperspectrally-calibrated spots across the scan line arealso defined by the CIE-specified wavelength interval (λ).

FIGS. 1 and 4 describe the primary optical, electro-optical andmechanical components and systems and their arrangement. FIG. 5describes the primary electronic and electro-mechanical components andtheir logical arrangement. FIG. 6 illustrates the process components andtheir relationship to the primary system components described in FIGS.1, 4 and 5. FIG. 7 describes the logical buffers and buffer data createdand used by the system and its processes, and FIGS. 8, 9, 10 and 11illustrate the methodology of the data conversion process, described byequations 1-4.

Before image scanning commences, the dynamic range of the system must beestablished by calibrating each light sensor pixel. With reference toFIG. 1, for calibration, the output 6 of the white light source 2 isdirected through the hyperspectral apparatus 5 in such a way as to allowa continuous portion of spectrum between the wavelengths of 360 nm and830 nm to pass through the device. With the magnification optics 19, 20set in the desired position, and no image 17 in the translation stage15, the intensity iris diaphragm 4 controlled by a servo motor 3 isadjusted by the application software 29 resident in the host computersystem 30 of FIG. 5. As illustrated in FIG. 5, application software 29in host computer system 30 adjusts iris diaphragm 4 by issuing digitalcommands to the servo motor 3 via the #4 servo amplifier 35. When thesignals exiting the light sensor equal its saturation point, the maximumamount of light the sensor is able to measure in a linear fashion beforeit overloads, or blooms, the White Point, WP, for the component, hasbeen determined.

Once WP is established, as illustrated in FIG. 5, the hyperspectralapparatus (monochromator) 5 adjusts, under the control of theapplication software 29, to output narrow bandpasses of spectrumdesignated WP_(λ), at the appropriate interval (λ). Assuming a 5 nmspectral interval, the system steps through the 360 nm-830 nm continuousspectrum in 5 nm bandpasses, saving a value for each sensor pixel ateach bandpass in the WP_(λ) Buffer 47 as illustrated in FIGS. 6 and 7.

Following collection of the WP_(λ), the intensity iris diaphragm 4 isclosed and the 95 measurements are repeated, thereby creating a valuefor each pixel at each hyperspectral bandpass (λ) that is designated theBlack Point, BP_(λ). BP_(λ) represents the threshold of electronicnoise, or the minimum amount of light the individual light sensor pixelscan measure. When the BP_(λ) is subtracted from the WP_(λ), theresulting value represents the calibrated linear dynamic range for eachpixel.

FIG. 8 illustrates how the WP_(λ) and BP_(λ), values are used tocalculate T_(λ), the percentage of light transmitted through the image17 and captured by individual pixels. At each hyperspectral bandpassinterval (λ) the portion of the image represented by the scan line isilluminated by a sweep of the spot. The light transmitted through theimage, collected by the sensor 24 and processed by the ADC 27 is rawcount digital data designated RCT_(λ) stored in RC buffer 49 asillustrated in FIGS. 6, 7 and 8. The BP_(λ) for the appropriate pixel atthe appropriate bandpass interval (λ) is then subtracted from theappropriate RCT_(λ), and this value is then divided by the result of theappropriate (WP_(λ)-BP_(λ)) operation (illustrated as calculation 55 inFIG. 8). This calculation produces T_(λ), the percentage of lighttransmitted through the image for the appropriate pixel at theappropriate bandpass interval (λ). This value is stored in the T_(λ)buffer 50 as illustrated in FIGS. 6, 7 and 8, and represents what theCIE defines as the spectral power distribution of the object, in the CIEsystem of measurement and transformation of color stimulus.

FIGS. 9, 10 and 11 describe how T_(λ) is then mathematically processedby the DSP 28 with spectral power distribution S_(λ) and color matchingfunctions x _(λ), y _(λ) and z _(λ) to express the CIE TristimulusValues X, Y and Z. Values for S_(λ), x _(λ), y _(λ) and z _(λ) arerespectively stored in Illuminant spectral power distribution buffer 41and Observer color matching function buffer 42 illustrated in FIGS. 6and 7.

As illustrated for example by FIGS. 6, 7 and 8, values from the T_(λ)buffer 50 for each bandpass interval (λ) are multiplied by DSP 29 withappropriate values from the appropriate Bandpass x, Bandpass y andBandpass z Transform Operator buffers 44, 45, 46 to produce the Bandpassdelineated intermediate values T_(λ)S_(λ) x _(λ), T_(λ)S_(λ) y _(λ) andT_(λ)S_(λ) z _(λ) held by in data buffers controlled by DSP 29 asillustrated in FIG. 9. As illustrated in FIG. 10, Bandpass delineatedintermediate values T_(λ)S_(λ) x _(λ), T_(λ)S_(λ) y _(λ) and T_(λ)S_(λ)z _(λ) are summed to respectively total Bandpass delineated sum valuesΣT_(λ)S_(λ) x _(λ), ΣT_(λ)S_(λ) y _(λ) and ΣT_(λ)S_(λ) z _(λ), andBandpass delineated sum values ΣT_(λ)S_(λ) x _(λ), ΣT_(λ)S_(λ) y _(λ)and ΣT_(λ)S_(λ) z _(λ) are respectively stored in Bandpass x, Bandpass yand Bandpass z buffers 51, 52, 53.

FIG. 11 illustrates the completion of the transformation of theindividual pixels of the active scan line buffers. Bandpass delineatedsum values ΣT_(λ)S_(λ) x _(λ), ΣT_(λ)S_(λ) y _(λ) and ΣT_(λ)S_(λ) z _(λ)are each multiplied by the appropriate normalization factor k fromnormalization function buffer 43 of FIGS. 6 and 7, with the finalproduct for each pixel in the scan line being computed as theTristimulus Values X, Y and Z. Tristimulus Values X, Y and Z are storedin XYZ buffer 54 illustrated in FIGS. 6 and 7.

The operator has the option of saving the T_(λ) values of each pixel incalorimetric characteristic curve file 61 of FIG. 6 before furtherprocessing into XYZ values. This file represents the spectral powerdistribution for each image pixel, and it may be further processed afterimage capture using any logical combination of Illuminant and Observer.This choice gives the user increased flexibility to transform the datato conform with specific reproduction requirements which may be unknownat the time of image capture, at the cost of a larger initial data file.

Once the colorimetric characteristic curve data, or T_(λ) values, havebeen reduced to XYZ Tristimulus Values, the user may specify otherCIE-defined transformations, for example, including the XYZ to CIELABtransform 62 (illustrated in FIG. 6), which the system can perform inreal-time as it is scanning an image to store in TIFF form in a CIELABencoded TIFF file 68.

As illustrated for example in FIGS. 5 and 6, application software 29operates digital signal processor (DSP) 28 to transform a pixel'sTristimulus Values X, Y and Z into a new set of three values, locatingthe pixel's color in the three-dimensional L*a*b* (CIELAB) color space,a device independent color space acknowledged to mathematicallyrepresent human color perception. The XYZ to CIELAB methodology ismathematically expressed as:L*=116(Y/Y _(n))^(1/3)−16  [5]a*=500[(X/X _(n))^(1/3)−(Y/Y _(n))^(1/3)]  [6]b*=200[(Y/Y _(n))^(1/3)−(Z/Z _(n))^(1/3)]  [7]where:X/X _(n) ; Y/Y _(n) ; Z/Z _(n)>0.01

and X_(n) Y_(n) Z_(n) are the Tristimulus values of the Illuminantselected with Y_(n) equal to 100 obtained by use of the samenormalization method used to obtain X, Y, Z.

When one or more of the ratios X/X_(n), Y/Y_(n), Z/Z_(n) is less than0.01 or if Y/Y_(n)≦0.008856 forL*=116(Y/Y _(n))^(1/3)−16  [5]ThenL*=903.3(Y/Y _(n)) where (Y/Y _(n))≦0.008856  [8]anda*=500[f(X/X _(n))−f(Y/Y _(n))]  [9]b*=200[f(Y/Y _(n))−f(Z/Z _(n))]  [10]Wheref(X/X _(n))=(X/X _(n))^(1/3) when X/X _(n)>0.008856 andJ(X/X _(n))=7.787(X/X _(n))+16/116 when X/X _(n)≦0.008856 andf(Y/Y _(n))=(Y/Y _(n))^(1/3) when Y/Y _(n)>0.008856 andf(Y/Y _(n))=7.787(Y/Y _(n))+16/116 when X/X _(n)≦0.008856 andf(Z/Z _(n))=(Z/Z _(n))^(1/3) when Z/Z _(n)>0.008856 andf(Z/Z _(n))=7.787(Z/Z _(n))+16/116 when Z/Z _(n) 0.008856.

The system user may also choose to have the XYZ values that aregenerated by the scanner stored as a data file 66 or transformed viamatrix operations 67 to additive device dependent RGB color values 63that also can be displayed by the host computer via the RGB buffer 64.Before this transformation can begin, the scanning system must beprovided with a matrix of values representing the XYZ values of theprimary colors of the target RGB system. The user may also choose totransform the XYZ values to subtractive CMYK device dependent colorvalues 65, via transform 67.

The foregoing describes the invention in terms of embodiments foreseenby the inventor for which an enabling description was available,notwithstanding that insubstantial modifications of the invention, notpresently foreseen, may nonetheless represent equivalents thereto.

1. A method for transforming light intensities measured by a sensor fora portion of a graphical image into a device-independent representationof color for the image portion, the method comprising the steps of:receiving by a processor an output signal from the sensor indicative ofa light intensity for the image portion for each of a plurality ofhyperspectral bandpasses, wherein the plurality of hyperspectralbandpasses define a spectrum characterized by wavelengths rangingcontinuously between 360 and 830 nanometers, and wherein the componentbeam for each hyperspectral bandpass is characterized by a substantiallyunique and non-overlapping selection of continuous wavelengths from thespectrum; converting each of the output signals for the plurality ofhyperspectral bandpasses into a raw count digital data (RCT_(λ)) value;calculating by another processor a relative normalized intensity value(T_(λ)) for each of the plurality of hyperspectral bandpasses as afunction of the RCT_(λ) value, a white point (WP_(λ)) value and a blackpoint (BP_(λ)) value, wherein the WP_(λ) value is indicative of a lightintensity that saturates the sensor and the BP_(λ) value is indicativeof a threshold electronic noise level of the sensor; storing the T_(λ)value for each of the plurality of hyperspectral bandpasses as ahyperspectral trace representing a spectral power distribution;calculating bandpass delineated intermediate values (T_(λ)S_(λ)x_(λ),T_(λ)S_(λ)y_(λ), T_(λ)S_(λ)z_(λ)) for each of the plurality ofhyperspectral bandpasses as a function of the T_(λ) value, auser-selected Illuminant value (S_(λ)) and user-selected Observer values(x_(λ), y_(λ), and z_(λ)); calculating bandpass delineated sum values(ΣT_(λ)S_(λ)x_(λ), ΣT_(λ)S_(λ)y_(λ), ΣT_(λ)S_(λ)z_(λ)) for the imageportion by respectively summing each of the bandpass delineatedintermediate values (T_(λ)S_(λ)x_(λ), T_(λ)S_(λ)y_(λ), T_(λ)S_(λ)z_(λ))for all of the plurality of hyperspectral bandpasses; calculatingtristimulus values (X, Y, Z) for the image portion respectively askΣT_(λ)S_(λ)x_(λ), kΣT_(λ)S_(λ)y_(λ), and kΣT_(λ)S_(λ)z_(λ), where aconstant k is calculated as 100/ΣS_(λ)y_(λ); and storing the tristimulusvalues (X, Y, Z) for the image.
 2. The method of claim 1, furthercomprising the steps of: calculating image values for the image portionfor one or more additional device-independent color space models as afunction of the stored tristimulus values (X, Y, Z); and storing thecalculated image values for each of the one or more additionaldevice-independent color space models as a computer-readable data file.3. The method of claim 2, wherein the one or more additionaldevice-independent color space models are selected from the groupconsisting of CIELAB, CIELUV and CIE xyY models.
 4. The method of claim1, further comprising the steps of: calculating image values for theimage portion for one or more device-dependent color space models as afunction of the stored tristimulus values (X, Y, Z); and storing thecalculated image values for each of the one or more additionaldevice-dependent color space models as a computer-readable data file. 5.The method of claim 4, wherein the one or more device-dependent colorspace models are selected from the group consisting of additive RGB andsubtractive CMYK models.
 6. The method of claim 1, wherein where thestored hyperspectral trace is used to recalculate alternate TristimulusValues X, Y, and Z in conjunction with one or more alternativelyselected Illuminant values (S_(λ)) and alternatively selected Observervalues ( x _(λ), y _(λ), and z _(λ)).